Sensor‐based activity recognition independent of device placement and orientation,Transactions on Emerging Telecommunications Technologies

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Sensor‐based activity recognition independent of device placement and orientation
Transactions on Emerging Telecommunications Technologies ( IF 2.5 ) Pub Date : 2020-01-10 , DOI: 10.1002/ett.3823
Junhao Shi ₁ , Decheng Zuo ₁ , Zhan Zhang ₁ , Danyan Luo ₁

Affiliation

Human activity recognition (HAR) is a prominent subfield of pervasive computing and also provides context of many applications such as healthcare, education, and entertainment. Most wearable HAR studies assume that sensing device placement and orientation are fixed and never change. However, this condition is actually not always guaranteed in the real scenario and recognition result is influenced by the distortion as consequence. To handle this, our work proposes a new model based on convolutional neural network to extract robust features which are invariant of device placement and orientation, to train machine learning classifiers. We first carry out experiments to show negative effects of this problem. Then, we apply the convolutional neural network–based hybrid structure on the HAR. Results show that our method provides 15% to 40% accuracy promotion on public data set and 10% to 20% promotion on our own data set, both with distortion.

中文翻译：

基于传感器的活动识别与设备的放置和方向无关

人类活动识别（HAR）是普适计算的一个重要子领域，还提供了许多应用程序的上下文，例如医疗保健，教育和娱乐。大多数可穿戴式HAR研究都假设传感设备的位置和方向是固定的，永远不会改变。但是，实际上在实际情况下并不能始终保证此条件，因此结果会受到失真的影响。为了解决这个问题，我们的工作提出了一个基于卷积神经网络的新模型，以提取设备放置和方向不变的鲁棒特征，以训练机器学习分类器。我们首先进行实验以显示此问题的负面影响。然后，我们在HAR上应用基于卷积神经网络的混合结构。

更新日期：2020-01-10

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